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Computational Systems Biology …Biology X – Lecture 1… Bud Mishra Professor of Computer Science, Computational Systems Biology …Biology X – Lecture 1… Bud Mishra Professor of Computer Science, Mathematics, & Cell Biology

Robert Hooke • Robert Hooke (1635 -1703) was an experimental scientist, mathematician, architect, and Robert Hooke • Robert Hooke (1635 -1703) was an experimental scientist, mathematician, architect, and astronomer. Secretary of the Royal Society from 1677 to 1682, … • Hooke was considered the “England’s Da Vinci” because of his wide range of interests. • His work Micrographia of 1665 contained his microscopical investigations, which included the first identification of biological cells. • In his drafts of Book II, Newton had referred to him as the most illustrious Hooke—”Cl[arissimus] Hookius. ” • Hooke became involved in a dispute with Isaac Newton over the priority of the discovery of the inverse square law of gravitation.

Hooke to Halley • “[Huygen’s Preface] is concerning those properties of gravity which I Hooke to Halley • “[Huygen’s Preface] is concerning those properties of gravity which I myself first discovered and showed to this Society and years since, which of late Mr. Newton has done me the favour to print and publish as his own inventions. “

Newton to Halley • “Now is this not very fine? Mathematicians that find out, Newton to Halley • “Now is this not very fine? Mathematicians that find out, settle & do all the business must content themselves with being nothing but dry calculators & drudges & another that does nothing but pretend & grasp at all things must carry away all the inventions… • “I beleive you would think him a man of a strange unsociable temper. ”

Newton to Hooke • “If I have seen further than other men, it is Newton to Hooke • “If I have seen further than other men, it is because I have stood on the shoulders of giants and you my dear Hooke, have not. " – Newton to Hooke

Image & Logic • The great distance between – a glimpsed truth and – Image & Logic • The great distance between – a glimpsed truth and – a demonstrated truth • Christopher Wren/Alexis Claude Clairaut

Micrographia ¦ Principia Micrographia ¦ Principia

Micrographia Micrographia

“The Brain & the Fancy” • “The truth is, the science of Nature has “The Brain & the Fancy” • “The truth is, the science of Nature has already been too long made only a work of the brain and the fancy. It is now high time that it should return to the plainness and soundness of observations on material and obvious things. ” – Robert Hooke. (1635 1703), Micrographia 1665

Principia Principia

“Induction & Hypothesis” • “Truth being uniform and always the same, it is admirable “Induction & Hypothesis” • “Truth being uniform and always the same, it is admirable to observe how easily we are enabled to make out very abstruse and difficult matters, when once true and genuine Principles are obtained. ” – Halley, “The true Theory of the Tides, extracted from that admired Treatise of Mr. Issac Newton, Intituled, Philosophiae Naturalis Principia Mathematica, ” Phil. Trans. 226: 445, 447. Hypotheses non fingo. I feign no hypotheses. Principia Mathematica. • This rule we must follow, that the argument of induction may not be evaded by hypotheses.

Morphogenesis Morphogenesis

Alan Turing: 1952 • “The Chemical Basis of Morphogenesis, ” 1952, Phil. Trans. Roy. Alan Turing: 1952 • “The Chemical Basis of Morphogenesis, ” 1952, Phil. Trans. Roy. Soc. of London, Series B: Biological Sciences, 237: 37— 72. • A reaction-diffusion model for development.

“A mathematical model for the growing embryo. ” • A very general program for “A mathematical model for the growing embryo. ” • A very general program for modeling embryogenesis: The `model’ is “a simplification and an idealization and consequently a falsification. ” • Morphogen: “is simply the kind of substance concerned in this theory…” in fact, anything that diffuses into the tissue and “somehow persuades it to develop along different lines from those which would have been followed in its absence” qualifies.

Diffusion equation first temporal derivative: rate ¶ a/¶ t = Da r 2 a Diffusion equation first temporal derivative: rate ¶ a/¶ t = Da r 2 a a: concentration Da: diffusion constant second spatial derivative: flux

Reaction-Diffusion ¶a/¶ t = f(a, b) + Da r 2 a f(a, b) = Reaction-Diffusion ¶a/¶ t = f(a, b) + Da r 2 a f(a, b) = a(b-1) –k 1 ¶ b/¶ t = g(a, b) + Db r 2 b g(a, b) = -ab +k 2 Turing, A. M. (1952). “The chemical basis of morphogenesis. “ Phil. Trans. Roy. Soc. London B 237: 37 a - b reaction diffusion

Reaction-diffusion: an example A fed at rate F d[A]/dt=F(1 -[A]) A+2 B ! 3 Reaction-diffusion: an example A fed at rate F d[A]/dt=F(1 -[A]) A+2 B ! 3 B B!P B extracted at rate F, decay at rate k d[B]/dt=-(F+k)[B] reaction: -d[A]/dt = d[B]/dt = [A][B]2 diffusion: d[A]/dt=DA 2[A]; d[B]/dt=DB 2[B] ¶ [A]/¶ t = F(1 -[A]) – [A][B]2 + DA 2[A] ¶ [B]/¶ t = -(F+k)[B] +[A][B]2 + DB 2[B] Pearson, J. E. : Complex patterns in simple systems. Science 261, 189 -192 (1993).

Reaction-diffusion: an example Reaction-diffusion: an example

Genes: 1952 • Since the role of genes is presumably catalytic, influencing only the Genes: 1952 • Since the role of genes is presumably catalytic, influencing only the rate of reactions, unless one is interested in comparison of organisms, they “may be eliminated from the discussion…”

Crick & Watson : 1953 Crick & Watson : 1953

Genome • Genome: – Hereditary information of an organism is encoded in its DNA Genome • Genome: – Hereditary information of an organism is encoded in its DNA and enclosed in a cell (unless it is a virus). All the information contained in the DNA of a single organism is its genome. • DNA molecule can be thought of as a very long sequence of nucleotides or bases: S = {A, T, C, G}

The Central Dogma DNA Transcription RNA Translation • The central dogma(due to Francis Crick The Central Dogma DNA Transcription RNA Translation • The central dogma(due to Francis Crick in 1958) states that these information flows are all unidirectional: “The central dogma states that once `information' has passed into protein it cannot get out again. The transfer of information from nucleic acid to nucleic acid, or from nucleic acid to protein, may be possible, but transfer from protein to protein, or from protein to nucleic acid is impossible. Information means here the precise determination of sequence, either of Protein bases in the nucleic acid or of amino acid residues in the protein. ”

RNA, Genes and Promoters • A specific region of DNA that determines the synthesis RNA, Genes and Promoters • A specific region of DNA that determines the synthesis of proteins (through the transcription and translation) is called a gene – Originally, a gene meant something more abstract---a unit of hereditary inheritance. – Now a gene has been given a physical molecular existence. • Transcription of a gene to a messenger RNA, m. RNA, is keyed by a transcriptional activator/factor, which attaches to a promoter (a specific sequence adjacent to the gene). • Regulatory sequences such as silencers and enhancers control the rate of transcription Promoter Terminator 10 -35 bp Transcriptional Initiation Gene Transcriptional Termination

“The Brain & the Fancy” “Work on the mathematics of growth as opposed to “The Brain & the Fancy” “Work on the mathematics of growth as opposed to the statistical description and comparison of growth, seems to me to have developed along two equally unprofitable lines… It is futile to conjure up in the imagination a system of differential equations for the purpose of accounting for facts which are not only very complex, but largely unknown, …What we require at the present time is more measurement and less theory. ” – Eric Ponder, Director, CSHL (LIBA), 19361941.

“Axioms of Platitudes” -E. B. Wilson 1. Science need not be mathematical. 2. Simply “Axioms of Platitudes” -E. B. Wilson 1. Science need not be mathematical. 2. Simply because a subject is mathematical it need not therefore be scientific. 3. Empirical curve fitting may be without other than classificatory significance. 4. Growth of an individual should not be confused with the growth of an aggregate (or average) of individuals. 5. Different aspects of the individual, or of the average, may have different types of growth curves.

Genes for Segmentation • Fertilization followed by cell division • Pattern formation – instructions Genes for Segmentation • Fertilization followed by cell division • Pattern formation – instructions for – Body plan (Axes: A-P, D-V) – Germ layers (ecto-, meso-, endoderm) • Cell movement - form – gastrulation • Cell differentiation

PI: Positional Information • Positional value – Morphogen – a substance – Threshold concentration PI: Positional Information • Positional value – Morphogen – a substance – Threshold concentration • Program for development – Generative rather than descriptive • “French-Flag Model”

bicoid • The bicoid gene provides an A-P morphogen gradient bicoid • The bicoid gene provides an A-P morphogen gradient

gap genes • The A-P axis is divided into broad regions by gap gene gap genes • The A-P axis is divided into broad regions by gap gene expression • The first zygotic genes • Respond to maternallyderived instructions • Short-lived proteins, gives bell-shaped distribution from source

Transcription Factors in Cascade • Hunchback (hb) , a gap gene, responds to the Transcription Factors in Cascade • Hunchback (hb) , a gap gene, responds to the dose of bicoid protein • A concentration above threshold of bicoid activates the expression of hb • The more bicoid transcripts, the further back hb expression goes

Transcription Factors in Cascade • Krüppel (Kr), a gap gene, responds to the dose Transcription Factors in Cascade • Krüppel (Kr), a gap gene, responds to the dose of hb protein • A concentration above minimum threshold of hb activates the expression of Kr • A concentration above maximum threshold of hb inactivates the expression of Kr

Segmentation • Parasegments are delimited by expression of pair -rule genes in a periodic Segmentation • Parasegments are delimited by expression of pair -rule genes in a periodic pattern • Each is expressed in a series of 7 transverse stripes

Pattern Formation – Edward Lewis, of the California Institute of Technology – Christiane Nuesslein-Volhard, Pattern Formation – Edward Lewis, of the California Institute of Technology – Christiane Nuesslein-Volhard, of Germany's Max-Planck Institute – Eric Wieschaus, at Princeton • Each of the three were involved in the early research to find the genes controlling development of the Drosophila fruit fly.

The Network of Interaction EN CID CN hh proteins WG positive interacions en PTC The Network of Interaction EN CID CN hh proteins WG positive interacions en PTC PH PH HH a cell m. RNA WG PTC ptc + cid wg en HH Cell-to-cell interface negative interacions a neighbor Legend: • WG=wingless • HH=hedgehog • CID=cubitus iterruptus • CN=repressor fragment of CID • PTC=patched • PH=patchedhedgehog complex

Completeness: von Dassow, Meir, Munro & Odell, 2000 • “We used computer simulations to Completeness: von Dassow, Meir, Munro & Odell, 2000 • “We used computer simulations to investigate whether the known interactions among segment polarity genes suffice to confer the properties expected of a developmental module…. • “Using only the solid lines in [earlier figure] we found no such parameter sets despite extensive efforts. . Thus the solid connections cannot suffice to explain even the most basic behavior of the segment polarity network… • “There must be active repression of en cells anterior to wg-expressing stripe and something that spatially biases the response of wg to Hh. There is a good evidence in Drosophila for wg autoactivation…”

Completeness • “We incorporated these two remedies first (light gray lines). With these links Completeness • “We incorporated these two remedies first (light gray lines). With these links installed there are many parameter sets that enable the model to reproduce the target behavior, so many that they can be found easily by random sampling. ”

Model Parameters Model Parameters

Complete Model Complete Model

Complete Model Complete Model

Is this your final answer? • It is not uncommon to assume certain biological Is this your final answer? • It is not uncommon to assume certain biological problems to have achieved a cognitive finality without rigorous justification. • Rigorous mathematical models with automated tools for reasoning, simulation, and computation can be of enormous help to uncover – cognitive flaws, – qualitative simplification or – overly generalized assumptions. • Some ideal candidates for such study would include: – – prion hypothesis cell cycle machinery muscle contractility processes involved in cancer (cell cycle regulation, angiogenesis, DNA repair, apoptosis, cellular senescence, tissue space modeling enzymes, etc. ) – signal transduction pathways, and many others.

Systems Biology Combining the mathematical rigor of numerology with the predictive power of astrology. Systems Biology Combining the mathematical rigor of numerology with the predictive power of astrology. Cyberia Numerlogy SYSTEMS BIOLOGY The Astrology Numeristan HOTzone Astrostan Infostan Interpretive Biology Integrative Biology Computational Biology Bioinformatics Bio. Spice

Computational Systems Biology How much of reasoning about biology can be automated? Computational Systems Biology How much of reasoning about biology can be automated?

Why do we need a tool? We claim that, by drawing upon mathematical approaches Why do we need a tool? We claim that, by drawing upon mathematical approaches developed in the context of dynamical systems, kinetic analysis, computational theory and logic, it is possible to create powerful simulation, analysis and reasoning tools for working biologists to be used in deciphering existing data, devising new experiments and ultimately, understanding functional properties of genomes, proteomes, cells, organs and organisms. Simulate Biologists! Not Biology!!

Reasoning and Experimentation Model ODE/SDE/ Hybrid Systems Model Construction Hypotheses In silico Results Model Reasoning and Experimentation Model ODE/SDE/ Hybrid Systems Model Construction Hypotheses In silico Results Model Simulation Comparison Revision Symbolic Analysis Reachability Analysis Simulation Temporal Logic Verification Experiment Design Experiment Runs Experimental Results

Future Biology • Biology of the future should only involve a biologist and his Future Biology • Biology of the future should only involve a biologist and his dog: the biologist to watch the biological experiments and understand the hypotheses that the data-analysis algorithms produce and the dog to bite him if he ever touches the experiments or the computers.

Simpathica is a modular system Canonical Form: Characteristics: • Predefined Modular Structure • Automated Simpathica is a modular system Canonical Form: Characteristics: • Predefined Modular Structure • Automated Translation from Graphical to Mathematical Model • Scalability

Glycolysis Glycogen P_i Glucose-1 -P Phosphorylase a Phosphoglucomutase Glucokinase Glucose-6 -P Phosphoglucose isomerase Fructose-6 Glycolysis Glycogen P_i Glucose-1 -P Phosphorylase a Phosphoglucomutase Glucokinase Glucose-6 -P Phosphoglucose isomerase Fructose-6 -P Phosphofructokinase

Formal Definition of S-system Formal Definition of S-system

An Artificial Clock • Three proteins: – Lac. I, tet. R & l c. An Artificial Clock • Three proteins: – Lac. I, tet. R & l c. I – Arranged in a cyclic manner (logically, not necessarily physically) so that the protein product of one gene is rpressor for the next gene. Lac. I! : tet. R; tet. R! Tet. R! : l c. I; l c. I ! l c. I! : lac. I; lac. I! Lac. I Leibler et al. , Guet et al. , Antoniotti et al. , Wigler & Mishra

Cycles of Repression • The first repressor protein, Lac. I from E. coli inhibits Cycles of Repression • The first repressor protein, Lac. I from E. coli inhibits the transcription of the second repressor gene, tet. R from the tetracycline-resistance transposon Tn 10, whose protein product in turn inhibits the expression of a third gene, c. I from l phage. • Finally, CI inhibits lac. I expression, • completing the cycle.

Biological Model • Standard molecular biology: Construct – A low-copy plasmid encoding the repressilator Biological Model • Standard molecular biology: Construct – A low-copy plasmid encoding the repressilator and – A compatible highercopy reporter plasmid containing the tetrepressible promoter PLtet 01 fused to an intermediate stability variant of gfp.

Cascade Model: Repressilator? x 1 - dx 2/dt = a 2 X 6 g Cascade Model: Repressilator? x 1 - dx 2/dt = a 2 X 6 g 26 X 1 g 21 - b 2 X 2 h 22 dx 4/dt = a 4 X 2 g 42 X 3 g 43 - b 4 X 4 h 44 dx 6/dt = a 6 X 4 g 64 X 5 g 65 - b 6 X 6 h 66 X 1, X 3, X 5 = const x 2 x 3 - x 4 x 5 - x 6

Sim. Pathica System Sim. Pathica System

Application: Purine Metabolism Application: Purine Metabolism

Purine Metabolism • Purine Metabolism – Provides the organism with building blocks for the Purine Metabolism • Purine Metabolism – Provides the organism with building blocks for the synthesis of DNA and RNA. – The consequences of a malfunctioning purine metabolism pathway are severe and can lead to death. • The entire pathway is almost closed but also quite complex. It contains – several feedback loops, – cross-activations and – reversible reactions • Thus is an ideal candidate for reasoning with computational tools.

Simple Model Simple Model

Biochemistry of Purine Metabolism • The main metabolite in purine biosynthesis is 5 -phosphoribosyl-a-1 Biochemistry of Purine Metabolism • The main metabolite in purine biosynthesis is 5 -phosphoribosyl-a-1 pyrophosphate (PRPP). – A linear cascade of reactions converts PRPP into inosine monophosphate (IMP). IMP is the central branch point of the purine metabolism pathway. – IMP is transformed into AMP and GMP. – Guanosine, adenosine and their derivatives are recycled (unless used elsewhere) into hypoxanthine (HX) and xanthine (XA). – XA is finally oxidized into uric acid (UA).

Purine Metabolism Purine Metabolism

Queries • Variation of the initial concentration of PRPP does not change the steady Queries • Variation of the initial concentration of PRPP does not change the steady state. (PRPP = 10 * PRPP 1) implies steady_state() • This query will be true when evaluated against the modified simulation run (i. e. the one where the initial concentration of PRPP is 10 times the initial concentration in the TRUE first run – PRPP 1). • Persistent increase in the initial concentration of PRPP does cause unwanted changes in the steady state values of some metabolites. • If the increase in the level of PRPP is in the order of 70% then the system does reach a steady state, and we expect to see increases in the levels of IMP and of the hypoxanthine pool in a “comparable” order of magnitude. Always (PRPP = 1. 7*PRPP 1) implies steady_state() TRUE

Queries • Consider the following statement: • Eventually (Always (PRPP = 1. 7 * Queries • Consider the following statement: • Eventually (Always (PRPP = 1. 7 * PRPP 1) implies steady_state() and Eventually Always(IMP < 2* IMP 1)) and Eventually (Always (hx_pool < 10*hx_pool 1))) • where IMP 1 and hx_pool 1 are the values observed in the unmodified trace. The above statement turns out to be false over the modified experiment trace. . • In fact, the increase in IMP is about 6. 5 fold while the hypoxanthine pool increase is about 60 fold. • Since the above queries turn out to be false over the modified trace, we conclude that the model “over-predicts” the increases in some of its products and that it should therefore be amended False

Final Model Final Model

Purine Metabolism Purine Metabolism

Computational Algebra & Differential Algebra Computational Algebra & Differential Algebra

Algebraic Approaches Algebraic Approaches

Differential Algebra Differential Algebra

Example System Example System

Input-Output Relations Input-Output Relations

Obstacles Obstacles

Issues • Symbolic Manipulation • Non-determinism • Hierarchy & Modularity Issues • Symbolic Manipulation • Non-determinism • Hierarchy & Modularity

Model-Checking Model-Checking

Verifying temporal properties Step 1. Formally encode the behavior of the system as a Verifying temporal properties Step 1. Formally encode the behavior of the system as a semi-algebraic hybrid automaton Step 2. Formally encode the properties of interest in TCTL Step 3. Automate the process of checking if the formal model of the system satisfies the formally encoded properties using quantifier elimination

Continuous-Time Logics • Linear Time – Metric Temporal Logic (MTL) – Timed Propositional Temporal Continuous-Time Logics • Linear Time – Metric Temporal Logic (MTL) – Timed Propositional Temporal Logic (TPTL) – Real-Time Temporal Logic (RTTL) – Explicit-Clock Temporal Logic (ECTL) – Metric Interval Temporal Logic (MITL) • Branching time – Real-Time Computation Tree Logic (RTCTL) – Timed Computation Tree Logic (TCTL) Alur et al,

Solution • Bounded Model Checking • Constrained Systems – Linear Systems – O-minimal – Solution • Bounded Model Checking • Constrained Systems – Linear Systems – O-minimal – SACo. Re (Semi algebraic Constrained Reset) – IDA (Independent Dynamics Automata) Lafferiere et al. , Piazza et al. , Casagrande et al.

Example Example

Example: Biological Pattern Formation • Embryonic Skin Of The South African Claw-Toed Frog • Example: Biological Pattern Formation • Embryonic Skin Of The South African Claw-Toed Frog • “Salt-and-Pepper” pattern formed due to lateral inhibition in the Xenopus epidermal layer where a regular set of ciliated cells form within a matrix of smooth epidermal cells

Delta-Notch Signalling Physically adjacent cells laterally inhibit each other’s ciliation (Delta production) Delta-Notch Signalling Physically adjacent cells laterally inhibit each other’s ciliation (Delta production)

Delta-Notch Pathway • Delta binds and activates its receptor Notch in neighboring cells (proteolytic Delta-Notch Pathway • Delta binds and activates its receptor Notch in neighboring cells (proteolytic release and nuclear translocation of the intracellular domain of Notch) • Activated Notch suppresses ligand (Delta) production in the cell • A cell producing more ligands forces its neighboring cells to produce less

Pattern formation by lateral inhibition with feedback: a mathematical model of Delta -Notch intercellular Pattern formation by lateral inhibition with feedback: a mathematical model of Delta -Notch intercellular signalling Collier et al. (1996) Rewriting… Where: Collier et al.

Hybrid Model: Delta-Notch States Q 1: Q 2: Both OFF Delta ON Q 3: Hybrid Model: Delta-Notch States Q 1: Q 2: Both OFF Delta ON Q 3: Q 4: Notch ON Both ON • Proteins are produced at a constant rate R (when their production is turned on) • Proteins degrade at a rate proportional (λ) to concentration

One-Cell Hybrid Automaton One-Cell Hybrid Automaton

One-Cell Hybrid Automaton One-Cell Hybrid Automaton

The Dynamics Of The 2 -Cell System… Tomlin et al. The Dynamics Of The 2 -Cell System… Tomlin et al.

2. 1 Continuous-State Equilibrium 2. 1 Continuous-State Equilibrium

2. 2 Discrete-State Equilibrium 2. 2 Discrete-State Equilibrium

To be continued… … To be continued… …